26 to 50 of 71 Retrieval-Augmented Generation Jobs in Central London

and defining success metrics. Contribute reusable components and frameworks for faster deployments. Qualifications 3+ years building production-grade AI/data solutions. Hands-on GenAI experience (LLMs, prompt engineering, RAG, vector databases, agentic frameworks like LangChain, LlamaIndex, DSPy). Strong Python proficiency with a GitHub portfolio showcasing MLE or GenAI projects. Expertise in Machine Learning Engineering (model development, deployment, monitoring More ❯

with productionising and deploying. Strong Python, SQL and programming skills Hands on experience working within areas such as NLP, LLM, recommendation systems Information Retrieval stacks, GenAI (specifically RAG systems). Familiar with architecture and implementation of 1+ ML frameworks (PyTorch, scikit-learn). Experience working in the full model lifecycle (including experimentation, training, testing, monitoring, and deployment). More ❯

Architect multi-step agent workflows using: Semantic Kernel SDK (C# or Python) Azure OpenAI (GPT-4, function calling, chat completion) Planner and Kernel Memory APIs for reasoning and memory RAG pipelines grounded in enterprise data via Azure AI Search Enterprise Data & AI Services Integration: Azure AI Search (vector indexing, hybrid search) Azure Form Recognizer for document understanding Azure Language Services More ❯

Must-Haves: Technical Chops 5+ years software engineering in production environments Strong in Node.js/TypeScript and Python Experience building microservices architectures Hands-on AI/ML integration (LLMs, RAG, agent frameworks) Cloud infrastructure expertise (GCP/AWS/Azure) Real-time systems and async processing experience Leadership 2+ years leading engineering teams (3-10 people) Proven track record hiring More ❯

Create and maintain technical documentation Communicate complex concepts to all stakeholders. At least 3 years of experience in AI solution engineering. Large Language Models experience including prompt engineering and RAG implementations. Expert data analytics, MLOps practices and API development. Desirable knowledge in Docker and Kubernetes More ❯

growth plans? Do you want to work in a team where innovation is a top priority? In this role you will: Develop and deploy AI-driven applications, integrating LLMs, RAG systems, and multi-agent frameworks. Build scalable data pipelines, APIs, and front-end interfaces to demonstrate real-world impact. Clean, transform, and model large structured and unstructured datasets. Collaborate with More ❯

decisions. Own operability: set up and maintain CI/CD, monitoring/alerting, performance & error budgets, and safe rollback paths. Build and integrate AI features (LLM/agent workflows, RAG/vector DB integration, eval hooks, cost/safety guardrails). Essential Requirements 3-6+ years building production backends/services , with shipped work and clear ownership. Strong Python More ❯

optimising prompts to ensure consistent, high-quality LLM outputs. Building and deploying AI-powered workflows that connect LLMs with business applications, APIs, and automation tools. Implementing RAG (Retrieval-Augmented Generation) pipelines to integrate enterprise data. Rapidly prototyping and iterating on AI solutions to demonstrate business value. Advising on responsible AI practices, governance, and compliance. … frameworks. Proficiency in Python or JavaScript for prototyping and integration work. Experience using automation platforms (UiPath, Power Automate, Zapier, n8n) and APIs. Knowledge of vector databases and embeddings for RAG pipelines. Excellent communication skills and the ability to translate business needs into technical solutions. This is a fantastic opportunity for an engineer who loves solving problems with AI and wants More ❯

categories AI You will have: Actively used AI in your workflows Deployed AI systems at scale Built real-world systems with LLMs Used agent frameworks, like LangGraph or LangChain, RAG pipelines It would be interesting if you have: Contributed to the AI/ML community (open-source, publications, talks) Experienced optimising AI models through fine-tuning, distillation, or human-in More ❯

team that’s pushing boundaries in autonomous AI What You’ll Need: A strong foundation in data science and machine learning Hands-on use of modern AI tools (LLMs, RAG, LangChain, co-pilots, agentic workflows) Curiosity and eagerness to learn in a fast-moving AI landscape Experience collaborating with stakeholders and translating business problems into technical solutions What You’ll More ❯

JavaScript, C#) Proven experience building solutions with the Microsoft Power Platform Familiarity with Azure or AWS A strong interest in AI/LLMs. Practical experience with Dify, or building RAG applications is a major plus. More ❯

AI capabilities at scale. Automating infrastructure/model deployments using Kubernetes, Terraform, and CI/CD pipelines. Building and managing vector databases and retrieval systems to support RAG pipelines. Working with LLM frameworks like LangChain , LangGraph , CrewAI , or similar tools. Partnering closely with engineering, security, and product teams to deliver safe, production-ready AI systems. What They’re More ❯

AI capabilities at scale. Automating infrastructure/model deployments using Kubernetes, Terraform, and CI/CD pipelines. Building and managing vector databases and retrieval systems to support RAG pipelines. Working with LLM frameworks like LangChain , LangGraph , CrewAI , or similar tools. Partnering closely with engineering, security, and product teams to deliver safe, production-ready AI systems. What They’re More ❯

Vision/NLP Strong Software Engineering skills (3 years+) Developed LLM architecture and deployed LLM applications Uptodate with current trends in AI Some experience with applying latest techniques like RAG architecture, GenAI, Parallel training etc The role is hybrid, with adhoc requirements to be on client premises (London) this could be between 1-3 days a week, so we would More ❯

agents that democratise access to powerful computational biology tools, enabling researchers worldwide to leverage cutting-edge models via intuitive chat interfaces. Who We Are: We are building next-generation generative models that learn the fundamentals of biology. Our team pursues ambitious scientific goals with curiosity and a deep commitment to research excellence. Team members have previously contributed to … and frameworks such as LangChain or LlamaIndex, or you’ve built custom agent frameworks from scratch. You understand intelligent information retrieval, with experience in RAG (Retrieval-Augmented Generation), vector databases, and embedding models for knowledge extraction. You can architect complex workflows using orchestration tools such as Airflow, Prefect, or Temporal, or by More ❯

semantic layers using dbt and Delta Live Tables to ensure consistency across analytics and AI use cases Enable Generative AI and ML workloads by designing pipelines for vector search, RAG, and feature engineering Implement secure access and governance controls including RBAC, SSO, token policies, and pseudonymisation frameworks Support batch and streaming data flows using technologies like Kafka, Airflow, and Terraform More ❯

optimizations. Your Profile: Essential skills/experience/knowledge: Experience in architecting and solutioning in Gen AI, Agentic AI, classic ML, and automation space. Good understanding of Prompt engineering, RAG pipelines, Supervised/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability. Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face, LLaMA, etc.) and vector databases More ❯

of AI/ML solutions. MLOps practices: CI/CD, model monitoring, retraining. Use of open-source and enterprise tools (LangChain, Azure OpenAI, Databricks). Generative AI features: embeddings, RAG, AI agents. Clean, testable code with modern engineering practices. Align with enterprise architecture and governance. Collaborate with architects and stakeholders. Lifecycle management of models. Pilot emerging technologies. Experience & Skills …/professional services experience is a plus. AI/ML frameworks: PyTorch, TensorFlow, LangChain. Cloud: Azure (preferred), AWS, GCP. MLOps: CI/CD, model lifecycle, monitoring. Generative AI: LLMs, RAG, chat agents. Data engineering alignment: ETL, governance. Strong coding, communication, and collaboration skills. Strategic thinking, problem-solving, and stakeholder engagement. More ❯

of AI/ML solutions. * MLOps practices: CI/CD, model monitoring, retraining. * Use of open-source and enterprise tools (LangChain, Azure OpenAI, Databricks). * Generative AI features: embeddings, RAG, AI agents. * Clean, testable code with modern engineering practices. * Align with enterprise architecture and governance. * Collaborate with architects and stakeholders. * Lifecycle management of models. * Pilot emerging technologies. Experience & Skills …/professional services experience is a plus. * AI/ML frameworks: PyTorch, TensorFlow, LangChain. * Cloud: Azure (preferred), AWS, GCP. * MLOps: CI/CD, model lifecycle, monitoring. * Generative AI: LLMs, RAG, chat agents. * Data engineering alignment: ETL, governance. * Strong coding, communication, and collaboration skills. * Strategic thinking, problem-solving, and stakeholder engagement. In accordance with the Employment Agencies and Employment Businesses Regulations More ❯

production-grade code shipping. Applied LLMs - Experience deploying multimodal foundation model-based products. Deep knowledge of LLM research, agents, tools, and function calling. Deploying LLMs - Experience with LangChain, indexing, RAG, semantic search, and fine-tuning (SFT/RL) of LLMs/agents. Why Join £100,000 – £170,000 base salary + meaningful equity Visa sponsorship and relocation support available A More ❯

function Comfort working in a fast-moving, ambiguous environment An eye for quality and a bias toward shipping Bonus: experience in building AI-powered tools or creative applications (e.g. RAG systems, assistants, agents, etc. More ❯

more about our culture on Role Summary We are seeking a Copyright Attorney to join our Legal team, focusing on data and content acquisitions for pre-training, post-training, RAG, and other AI applications. This is a new role, designed to provide legal support for the increasing number of licensing, data access, and partnership deals that fuel Mistral's AI More ❯

is integral to their agentic AI solutions. Requirements: Previous experience leading, managing and growing teams of AI Researchers and Engineers Strong technical knowledge and experience around AI agents, LLMs, RAG systems, reinforcement learning, fine-tuning, etc Data Engineering and Infrastructure knowledge and experience Strong product mindset Experience working in a B2B SaaS start-up or scale-up would be advantageous More ❯

Lead AI Engineer/Consultant - Greenfield Outside IR35 £600 - £750pd flexible for the right person RAG experience a must. About the Company Market leading SaaS organisation building out their AI function. About the Role This project is a greenfield build out of AI capabilities. They are starting with RAG use cases as a low bar, but are targeting a rich … deliver an AI capability. Lead design, architecture, and delivery of advanced AI/ML and generative AI solutions, ensuring scalable, secure, and production-ready system. Expert in MCP and RAG patterns. Design and build robust data and ingestion pipelines, integrate vector databases, and RSG. Expert-level proficiency in Python and key ML libraries (Langchain, Semantic Kernel, PyTorch, TensorFlow). Hands … complex, data-rich environments. Skilled in setting team engineering practices: Git, CI/CD, automated testing for ML code, code reviews, and documentation. Preferred Skills Expert in MCP and RAG patterns . Experience in developing agentic AI systems Lead tech enablement and mentor engineers, fostering culture of reliability, continuous improvement, and collaboration. Excellent communication skills, able to translate technical strategy More ❯

AI capabilities at scale. We’re hiring an experienced, hands-on Machine Learning Engineer to architect and deliver production-grade AI system — with a strong focus on MCP, RAG, and real-world deployment . This project is a green field build out of AI capabilities. Whoever joins has an opportunity to work on the foundational architectural definition and implementation. … the Director of Engineering Architect, build and ship advanced AI/ML & Generative AI solutions — scalable, secure, production-ready Design data ingestion pipelines, integrate vector databases and retrieval-augmented systems Ship models via APIs, containers, or cloud-native services Own engineering excellence — Git, CI/CD, automated ML testing, IaC Influence technical direction and mentor other … translate AI strategy into measurable business outcomes What you bring Expert Python — LangChain, Semantic Kernel, PyTorch, TensorFlow Hands-on cloud delivery — Azure/AWS, Terraform, ECS Proven experience building RAG/MCP architectures 5+ years in applied ML or AI engineering roles Send us your profile now for immediate consideration. November start. More ❯